Abstract
BackgroundThis paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles.ResultsOur tests show that this tool detects more similarities between protein families of distant homology than the previous primary sequence-based method. A very significant improvement in performance is observed when the real secondary structure is used.ConclusionsIntegration of primary and secondary structure information can substantially improve detection of relationships between remotely related protein families.
Highlights
This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information
Our method extends our previous work on profile-profile comparison [19]
Data sets We use a data set of domain families derived from the SCOP classification of protein structures [20], release 1.50
Summary
Protein family comparison using statistical models and predicted structural information. Address: Department of Computer Science, Cornell University, Ithaca, NY 14850, USA. Published: 25 November 2004 BMC Bioinformatics 2004, 5:183 doi:10.1186/1471-2105-5-183
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